This is my third post about point margins. At the risk of boring my readers with the same, seemingly mundane topic, I decided to write about it because I believe understanding point margins is of vital importance to formulating successful ATS betting strategies.
Let’s suppose we are to make an ATS bet on a game between Team H (home team) and Team A (away team).
Hypothetically, let’s say we suspect that Team H will win the game and take the “W” at home. If Team H is handicapped by the oddsmakers, it is crucial to have a rough estimate of points margin Team H will generate against Team A. How do you get that estimate?
There are various estimation methods. One simple, yet effective way is understanding the distribution of point margins of all the other teams winning at home. Once we understand this distribution, we can see how likely (or unlikely) Team H will cover the point spread.
We can apply the same thought process if we suspect that Team A will win the game and take the “W” as a visiting team. We can get the distribution of all the other teams winning at away in order to understand how likely (or unlikely) Team A will cover the point spread.
Having this sort of understanding is crucial in point spread betting, and I think every person who’s placing a bet should have a thorough understanding of point margins inside and out.
Enough about why it’s so important. Below is the analysis result from examining regular-season games data from the 1980-81 season through the 2017-18 season.
Winning Game Location |
Point Margin 25% Quantile |
Point Margin 50% Quantile |
Point Margin 75% Quantile |
Point Margin Average |
---|---|---|---|---|
Winning @ Home | 6 | 10 | 16 | 11.7 |
Winning @ Away | 5 | 8 | 13 | 9.87 |
Below is the same chart but with using playoffs-season games data.
Winning Game Location |
Point Margin 25% Quantile |
Point Margin 50% Quantile |
Point Margin 75% Quantile |
Point Margin Average |
---|---|---|---|---|
Winning @ Home | 6 | 10 | 16 | 11.9 |
Winning @ Away | 4 | 7 | 12 | 8.9 |
You can find the code I used to produce this analysis in my Github repository.